Overview

Dataset statistics

Number of variables23
Number of observations7905
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.7 MiB
Average record size in memory225.4 B

Variable types

Numeric13
Categorical10

Alerts

Age is highly overall correlated with Age_Years and 1 other fieldsHigh correlation
Age_Years is highly overall correlated with Age and 1 other fieldsHigh correlation
Ascites is highly overall correlated with Edema_N and 1 other fieldsHigh correlation
Bilirubin is highly overall correlated with CopperHigh correlation
Copper is highly overall correlated with BilirubinHigh correlation
Diagnosis_Date is highly overall correlated with Age and 1 other fieldsHigh correlation
Edema_N is highly overall correlated with Ascites and 2 other fieldsHigh correlation
Edema_S is highly overall correlated with Edema_NHigh correlation
Edema_Y is highly overall correlated with Ascites and 1 other fieldsHigh correlation
Hepatomegaly is highly overall correlated with StageHigh correlation
Stage is highly overall correlated with HepatomegalyHigh correlation
Edema_N is highly imbalanced (55.0%)Imbalance
Edema_S is highly imbalanced (71.2%)Imbalance
Edema_Y is highly imbalanced (74.1%)Imbalance
Sex is highly imbalanced (62.7%)Imbalance
Ascites is highly imbalanced (72.2%)Imbalance

Reproduction

Analysis started2024-02-12 06:31:20.440654
Analysis finished2024-02-12 06:31:35.327910
Duration14.89 seconds
Software versionydata-profiling vv4.6.4
Download configurationconfig.json

Variables

N_Days
Real number (ℝ)

Distinct461
Distinct (%)5.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2030.1733
Minimum41
Maximum4795
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size381.6 KiB
2024-02-12T00:31:35.402685image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum41
5-th percentile334
Q11230
median1831
Q32689
95-th percentile4127
Maximum4795
Range4754
Interquartile range (IQR)1459

Descriptive statistics

Standard deviation1094.2337
Coefficient of variation (CV)0.53898539
Kurtosis-0.49401726
Mean2030.1733
Median Absolute Deviation (MAD)724
Skewness0.44865975
Sum16048520
Variance1197347.5
MonotonicityNot monotonic
2024-02-12T00:31:35.511418image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1216 117
 
1.5%
1434 105
 
1.3%
769 83
 
1.0%
3445 73
 
0.9%
1765 64
 
0.8%
1785 64
 
0.8%
1363 60
 
0.8%
904 59
 
0.7%
334 58
 
0.7%
2294 56
 
0.7%
Other values (451) 7166
90.7%
ValueCountFrequency (%)
41 13
0.2%
51 16
0.2%
71 14
0.2%
76 1
 
< 0.1%
77 21
0.3%
78 1
 
< 0.1%
108 1
 
< 0.1%
110 25
0.3%
121 1
 
< 0.1%
124 1
 
< 0.1%
ValueCountFrequency (%)
4795 7
 
0.1%
4556 51
0.6%
4523 15
 
0.2%
4509 41
0.5%
4500 28
0.4%
4467 14
 
0.2%
4459 19
 
0.2%
4453 22
0.3%
4427 14
 
0.2%
4392 1
 
< 0.1%

Age
Real number (ℝ)

HIGH CORRELATION 

Distinct391
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18373.146
Minimum9598
Maximum28650
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size381.6 KiB
2024-02-12T00:31:35.629782image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum9598
5-th percentile12307
Q115574
median18713
Q320684
95-th percentile24622
Maximum28650
Range19052
Interquartile range (IQR)5110

Descriptive statistics

Standard deviation3679.9587
Coefficient of variation (CV)0.20029007
Kurtosis-0.49738238
Mean18373.146
Median Absolute Deviation (MAD)2604
Skewness0.084091298
Sum1.4523972 × 108
Variance13542096
MonotonicityNot monotonic
2024-02-12T00:31:35.741429image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22369 79
 
1.0%
22388 71
 
0.9%
20684 71
 
0.9%
19060 70
 
0.9%
16279 66
 
0.8%
20459 65
 
0.8%
19246 62
 
0.8%
14161 62
 
0.8%
22960 61
 
0.8%
23331 61
 
0.8%
Other values (381) 7237
91.5%
ValueCountFrequency (%)
9598 18
0.2%
10550 17
0.2%
10795 7
 
0.1%
10810 1
 
< 0.1%
10958 1
 
< 0.1%
11058 33
0.4%
11167 10
 
0.1%
11273 19
0.2%
11330 1
 
< 0.1%
11462 19
0.2%
ValueCountFrequency (%)
28650 36
0.5%
28018 5
 
0.1%
27398 22
0.3%
27394 1
 
< 0.1%
27239 1
 
< 0.1%
27220 23
0.3%
26580 8
 
0.1%
26567 1
 
< 0.1%
26259 13
 
0.2%
25899 20
0.3%

Bilirubin
Real number (ℝ)

HIGH CORRELATION 

Distinct111
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5944845
Minimum0.3
Maximum28
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size381.6 KiB
2024-02-12T00:31:35.851158image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0.3
5-th percentile0.5
Q10.7
median1.1
Q33
95-th percentile11
Maximum28
Range27.7
Interquartile range (IQR)2.3

Descriptive statistics

Standard deviation3.8129603
Coefficient of variation (CV)1.4696408
Kurtosis12.908824
Mean2.5944845
Median Absolute Deviation (MAD)0.5
Skewness3.3396953
Sum20509.4
Variance14.538666
MonotonicityNot monotonic
2024-02-12T00:31:35.956546image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.6 847
 
10.7%
0.7 653
 
8.3%
0.8 613
 
7.8%
0.9 608
 
7.7%
0.5 552
 
7.0%
1.1 443
 
5.6%
1.3 368
 
4.7%
1 292
 
3.7%
0.4 180
 
2.3%
1.4 175
 
2.2%
Other values (101) 3174
40.2%
ValueCountFrequency (%)
0.3 52
 
0.7%
0.4 180
 
2.3%
0.5 552
7.0%
0.6 847
10.7%
0.7 653
8.3%
0.8 613
7.8%
0.9 608
7.7%
1 292
 
3.7%
1.1 443
5.6%
1.2 166
 
2.1%
ValueCountFrequency (%)
28 13
0.2%
25.5 13
0.2%
24.5 16
0.2%
22.5 16
0.2%
21.9 1
 
< 0.1%
21.6 19
0.2%
21.4 1
 
< 0.1%
20 4
 
0.1%
18 4
 
0.1%
17.9 9
0.1%

Cholesterol
Real number (ℝ)

Distinct226
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean350.56192
Minimum120
Maximum1775
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size381.6 KiB
2024-02-12T00:31:36.062994image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum120
5-th percentile198
Q1248
median298
Q3390
95-th percentile646
Maximum1775
Range1655
Interquartile range (IQR)142

Descriptive statistics

Standard deviation195.37934
Coefficient of variation (CV)0.5573319
Kurtosis18.162327
Mean350.56192
Median Absolute Deviation (MAD)62
Skewness3.6796575
Sum2771192
Variance38173.088
MonotonicityNot monotonic
2024-02-12T00:31:36.167506image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
448 152
 
1.9%
248 151
 
1.9%
263 143
 
1.8%
298 138
 
1.7%
232 131
 
1.7%
260 120
 
1.5%
257 117
 
1.5%
316 110
 
1.4%
236 109
 
1.4%
280 106
 
1.3%
Other values (216) 6628
83.8%
ValueCountFrequency (%)
120 10
 
0.1%
127 18
 
0.2%
132 36
0.5%
134 1
 
< 0.1%
149 7
 
0.1%
151 9
 
0.1%
168 9
 
0.1%
172 19
 
0.2%
174 20
 
0.3%
175 58
0.7%
ValueCountFrequency (%)
1775 11
0.1%
1712 19
0.2%
1600 22
0.3%
1492 1
 
< 0.1%
1480 11
0.1%
1436 1
 
< 0.1%
1336 9
0.1%
1276 21
0.3%
1236 1
 
< 0.1%
1128 14
0.2%

Albumin
Real number (ℝ)

Distinct160
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.5483226
Minimum1.96
Maximum4.64
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size381.6 KiB
2024-02-12T00:31:36.273351image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1.96
5-th percentile2.97
Q13.35
median3.58
Q33.77
95-th percentile4.08
Maximum4.64
Range2.68
Interquartile range (IQR)0.42

Descriptive statistics

Standard deviation0.34617081
Coefficient of variation (CV)0.097559002
Kurtosis1.3396217
Mean3.5483226
Median Absolute Deviation (MAD)0.21
Skewness-0.5611495
Sum28049.49
Variance0.11983423
MonotonicityNot monotonic
2024-02-12T00:31:36.406565image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.35 370
 
4.7%
3.6 368
 
4.7%
3.7 326
 
4.1%
3.85 255
 
3.2%
3.5 223
 
2.8%
3.77 217
 
2.7%
3.26 195
 
2.5%
3.65 183
 
2.3%
3.61 166
 
2.1%
3.2 161
 
2.0%
Other values (150) 5441
68.8%
ValueCountFrequency (%)
1.96 4
 
0.1%
2.1 4
 
0.1%
2.23 3
 
< 0.1%
2.27 4
 
0.1%
2.31 4
 
0.1%
2.33 16
 
0.2%
2.35 1
 
< 0.1%
2.43 50
0.6%
2.52 1
 
< 0.1%
2.53 9
 
0.1%
ValueCountFrequency (%)
4.64 20
0.3%
4.52 5
 
0.1%
4.4 14
 
0.2%
4.38 24
0.3%
4.34 1
 
< 0.1%
4.31 1
 
< 0.1%
4.3 42
0.5%
4.26 1
 
< 0.1%
4.24 12
 
0.2%
4.23 19
0.2%

Copper
Real number (ℝ)

HIGH CORRELATION 

Distinct171
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean83.902846
Minimum4
Maximum588
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size381.6 KiB
2024-02-12T00:31:36.510815image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile14
Q139
median63
Q3102
95-th percentile231
Maximum588
Range584
Interquartile range (IQR)63

Descriptive statistics

Standard deviation75.899266
Coefficient of variation (CV)0.90460895
Kurtosis10.21299
Mean83.902846
Median Absolute Deviation (MAD)26
Skewness2.7017358
Sum663252
Variance5760.6986
MonotonicityNot monotonic
2024-02-12T00:31:36.618287image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
67 311
 
3.9%
52 303
 
3.8%
39 216
 
2.7%
58 207
 
2.6%
75 188
 
2.4%
41 179
 
2.3%
13 172
 
2.2%
20 169
 
2.1%
44 154
 
1.9%
38 151
 
1.9%
Other values (161) 5855
74.1%
ValueCountFrequency (%)
4 12
 
0.2%
5 2
 
< 0.1%
9 53
 
0.7%
10 25
 
0.3%
11 60
 
0.8%
12 36
 
0.5%
13 172
2.2%
14 42
 
0.5%
15 11
 
0.1%
16 7
 
0.1%
ValueCountFrequency (%)
588 19
0.2%
558 7
 
0.1%
464 26
0.3%
456 1
 
< 0.1%
444 21
0.3%
412 13
 
0.2%
380 43
0.5%
358 21
0.3%
308 4
 
0.1%
290 20
0.3%

Alk_Phos
Real number (ℝ)

Distinct364
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1816.7452
Minimum289
Maximum13862.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size381.6 KiB
2024-02-12T00:31:36.731632image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum289
5-th percentile614
Q1834
median1181
Q31857
95-th percentile6064.8
Maximum13862.4
Range13573.4
Interquartile range (IQR)1023

Descriptive statistics

Standard deviation1903.7507
Coefficient of variation (CV)1.0478908
Kurtosis11.59975
Mean1816.7452
Median Absolute Deviation (MAD)460
Skewness3.1955577
Sum14361371
Variance3624266.6
MonotonicityNot monotonic
2024-02-12T00:31:36.862474image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
663 117
 
1.5%
1345 81
 
1.0%
7277 79
 
1.0%
944 78
 
1.0%
794 76
 
1.0%
645 76
 
1.0%
1636 76
 
1.0%
1052 75
 
0.9%
2276 63
 
0.8%
674 63
 
0.8%
Other values (354) 7121
90.1%
ValueCountFrequency (%)
289 32
0.4%
310 10
 
0.1%
369 21
0.3%
377 17
0.2%
414 8
 
0.1%
423 31
0.4%
453 26
0.3%
466 16
0.2%
516 12
 
0.2%
554 31
0.4%
ValueCountFrequency (%)
13862.4 15
0.2%
13486.2 1
 
< 0.1%
12258.8 26
0.3%
11552 11
0.1%
11320.2 15
0.2%
11046.6 12
0.2%
10795.4 1
 
< 0.1%
10396.8 22
0.3%
10165 11
0.1%
9933.2 3
 
< 0.1%

SGOT
Real number (ℝ)

Distinct206
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean114.6046
Minimum26.35
Maximum457.25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size381.6 KiB
2024-02-12T00:31:36.973019image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum26.35
5-th percentile54.25
Q175.95
median108.5
Q3137.95
95-th percentile198.4
Maximum457.25
Range430.9
Interquartile range (IQR)62

Descriptive statistics

Standard deviation48.790945
Coefficient of variation (CV)0.42573286
Kurtosis5.8167874
Mean114.6046
Median Absolute Deviation (MAD)31
Skewness1.5348057
Sum905949.38
Variance2380.5563
MonotonicityNot monotonic
2024-02-12T00:31:37.087878image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
71.3 256
 
3.2%
57.35 247
 
3.1%
137.95 206
 
2.6%
120.9 198
 
2.5%
97.65 189
 
2.4%
170.5 184
 
2.3%
93 178
 
2.3%
128.65 170
 
2.2%
66.65 138
 
1.7%
106.95 137
 
1.7%
Other values (196) 6002
75.9%
ValueCountFrequency (%)
26.35 8
 
0.1%
28.38 12
 
0.2%
40.6 1
 
< 0.1%
41.85 16
 
0.2%
43.4 40
0.5%
45 14
 
0.2%
46.5 6
 
0.1%
49.6 52
0.7%
51.15 57
0.7%
52 15
 
0.2%
ValueCountFrequency (%)
457.25 17
0.2%
338 9
0.1%
328.6 15
0.2%
299.15 6
 
0.1%
288 9
0.1%
280.55 15
0.2%
272.8 9
0.1%
260.15 1
 
< 0.1%
253 1
 
< 0.1%
246.45 13
0.2%

Tryglicerides
Real number (ℝ)

Distinct154
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean115.34016
Minimum33
Maximum598
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size381.6 KiB
2024-02-12T00:31:37.183295image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum33
5-th percentile56
Q184
median104
Q3139
95-th percentile210
Maximum598
Range565
Interquartile range (IQR)55

Descriptive statistics

Standard deviation52.530402
Coefficient of variation (CV)0.45543894
Kurtosis15.048118
Mean115.34016
Median Absolute Deviation (MAD)27
Skewness2.6339208
Sum911764
Variance2759.4431
MonotonicityNot monotonic
2024-02-12T00:31:37.287445image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
90 262
 
3.3%
85 223
 
2.8%
91 218
 
2.8%
118 211
 
2.7%
68 188
 
2.4%
56 187
 
2.4%
146 181
 
2.3%
108 175
 
2.2%
55 171
 
2.2%
133 170
 
2.2%
Other values (144) 5919
74.9%
ValueCountFrequency (%)
33 13
 
0.2%
44 37
 
0.5%
46 12
 
0.2%
49 13
 
0.2%
50 19
 
0.2%
52 24
 
0.3%
53 15
 
0.2%
55 171
2.2%
56 187
2.4%
57 10
 
0.1%
ValueCountFrequency (%)
598 13
0.2%
432 16
0.2%
393 1
 
< 0.1%
382 4
 
0.1%
322 5
 
0.1%
319 15
0.2%
318 18
0.2%
309 20
0.3%
283 1
 
< 0.1%
280 20
0.3%

Platelets
Real number (ℝ)

Distinct227
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean265.22897
Minimum62
Maximum563
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size381.6 KiB
2024-02-12T00:31:37.390948image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum62
5-th percentile128
Q1211
median265
Q3316
95-th percentile430
Maximum563
Range501
Interquartile range (IQR)105

Descriptive statistics

Standard deviation87.465579
Coefficient of variation (CV)0.32977385
Kurtosis0.33057783
Mean265.22897
Median Absolute Deviation (MAD)53
Skewness0.42004793
Sum2096635
Variance7650.2274
MonotonicityNot monotonic
2024-02-12T00:31:37.494389image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
344 233
 
2.9%
228 159
 
2.0%
268 158
 
2.0%
295 154
 
1.9%
336 147
 
1.9%
251 144
 
1.8%
265 138
 
1.7%
269 136
 
1.7%
213 136
 
1.7%
309 132
 
1.7%
Other values (217) 6368
80.6%
ValueCountFrequency (%)
62 11
 
0.1%
65 1
 
< 0.1%
70 10
 
0.1%
71 15
 
0.2%
76 1
 
< 0.1%
79 18
0.2%
80 25
0.3%
81 11
 
0.1%
88 3
 
< 0.1%
95 38
0.5%
ValueCountFrequency (%)
563 36
0.5%
539 5
 
0.1%
518 14
 
0.2%
515 2
 
< 0.1%
514 13
 
0.2%
493 17
0.2%
487 10
 
0.1%
474 17
0.2%
471 24
0.3%
467 40
0.5%

Prothrombin
Real number (ℝ)

Distinct49
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.629462
Minimum9
Maximum18
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size381.6 KiB
2024-02-12T00:31:37.601722image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile9.6
Q110
median10.6
Q311
95-th percentile12
Maximum18
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.78173483
Coefficient of variation (CV)0.073544155
Kurtosis4.288955
Mean10.629462
Median Absolute Deviation (MAD)0.5
Skewness1.292436
Sum84025.9
Variance0.61110934
MonotonicityNot monotonic
2024-02-12T00:31:37.706784image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
10.6 1070
 
13.5%
11 842
 
10.7%
10 638
 
8.1%
9.9 517
 
6.5%
9.8 440
 
5.6%
10.1 390
 
4.9%
10.9 339
 
4.3%
11.5 295
 
3.7%
9.6 288
 
3.6%
10.2 283
 
3.6%
Other values (39) 2803
35.5%
ValueCountFrequency (%)
9 8
 
0.1%
9.1 9
 
0.1%
9.2 5
 
0.1%
9.3 8
 
0.1%
9.4 17
 
0.2%
9.5 137
 
1.7%
9.6 288
3.6%
9.7 199
 
2.5%
9.8 440
5.6%
9.9 517
6.5%
ValueCountFrequency (%)
18 1
 
< 0.1%
17.1 2
 
< 0.1%
15.2 12
 
0.2%
14.1 4
 
0.1%
13.6 9
 
0.1%
13.4 1
 
< 0.1%
13.3 6
 
0.1%
13.2 32
0.4%
13.1 1
 
< 0.1%
13 45
0.6%

Status
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size381.6 KiB
0
4965 
2
2665 
1
 
275

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters7905
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row0
3rd row2
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4965
62.8%
2 2665
33.7%
1 275
 
3.5%

Length

2024-02-12T00:31:37.799284image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-12T00:31:37.877594image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 4965
62.8%
2 2665
33.7%
1 275
 
3.5%

Most occurring characters

ValueCountFrequency (%)
0 4965
62.8%
2 2665
33.7%
1 275
 
3.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7905
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4965
62.8%
2 2665
33.7%
1 275
 
3.5%

Most occurring scripts

ValueCountFrequency (%)
Common 7905
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4965
62.8%
2 2665
33.7%
1 275
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7905
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4965
62.8%
2 2665
33.7%
1 275
 
3.5%

Diagnosis_Date
Real number (ℝ)

HIGH CORRELATION 

Distinct4621
Distinct (%)58.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16342.973
Minimum5755
Maximum28451
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size381.6 KiB
2024-02-12T00:31:37.969554image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum5755
5-th percentile10212
Q113293
median16320
Q318947
95-th percentile23001.8
Maximum28451
Range22696
Interquartile range (IQR)5654

Descriptive statistics

Standard deviation3945.0917
Coefficient of variation (CV)0.24139376
Kurtosis-0.44258475
Mean16342.973
Median Absolute Deviation (MAD)2843
Skewness0.16929262
Sum1.291912 × 108
Variance15563749
MonotonicityNot monotonic
2024-02-12T00:31:38.073953image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22035 45
 
0.6%
18634 36
 
0.5%
18822 34
 
0.4%
21484 33
 
0.4%
17935 33
 
0.4%
18291 30
 
0.4%
26885 25
 
0.3%
12715 24
 
0.3%
12139 23
 
0.3%
15255 21
 
0.3%
Other values (4611) 7601
96.2%
ValueCountFrequency (%)
5755 1
 
< 0.1%
6511 1
 
< 0.1%
6591 2
 
< 0.1%
6611 1
 
< 0.1%
6626 1
 
< 0.1%
6711 1
 
< 0.1%
6727 7
0.1%
6983 1
 
< 0.1%
7142 1
 
< 0.1%
7235 1
 
< 0.1%
ValueCountFrequency (%)
28451 1
 
< 0.1%
27720 1
 
< 0.1%
27697 1
 
< 0.1%
27650 1
 
< 0.1%
27630 1
 
< 0.1%
27618 1
 
< 0.1%
27283 1
 
< 0.1%
27215 1
 
< 0.1%
27200 1
 
< 0.1%
26885 25
0.3%

Age_Years
Real number (ℝ)

HIGH CORRELATION 

Distinct49
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.308033
Minimum26
Maximum78
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size381.6 KiB
2024-02-12T00:31:38.175780image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum26
5-th percentile34
Q143
median51
Q357
95-th percentile67
Maximum78
Range52
Interquartile range (IQR)14

Descriptive statistics

Standard deviation10.085483
Coefficient of variation (CV)0.20047461
Kurtosis-0.49383657
Mean50.308033
Median Absolute Deviation (MAD)7
Skewness0.082175664
Sum397685
Variance101.71697
MonotonicityNot monotonic
2024-02-12T00:31:38.283072image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
56 549
 
6.9%
53 401
 
5.1%
61 388
 
4.9%
52 340
 
4.3%
50 338
 
4.3%
41 335
 
4.2%
51 292
 
3.7%
46 275
 
3.5%
57 274
 
3.5%
49 256
 
3.2%
Other values (39) 4457
56.4%
ValueCountFrequency (%)
26 18
 
0.2%
29 17
 
0.2%
30 42
 
0.5%
31 68
 
0.9%
32 54
 
0.7%
33 142
1.8%
34 160
2.0%
35 182
2.3%
36 113
1.4%
37 128
1.6%
ValueCountFrequency (%)
78 36
0.5%
77 5
 
0.1%
75 47
0.6%
73 9
 
0.1%
72 13
 
0.2%
71 64
0.8%
70 68
0.9%
69 40
0.5%
68 80
1.0%
67 85
1.1%

Edema_N
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size381.6 KiB
1.0
7161 
0.0
744 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters23715
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row0.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 7161
90.6%
0.0 744
 
9.4%

Length

2024-02-12T00:31:38.377293image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-12T00:31:38.448101image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0 7161
90.6%
0.0 744
 
9.4%

Most occurring characters

ValueCountFrequency (%)
0 8649
36.5%
. 7905
33.3%
1 7161
30.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15810
66.7%
Other Punctuation 7905
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 8649
54.7%
1 7161
45.3%
Other Punctuation
ValueCountFrequency (%)
. 7905
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 23715
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 8649
36.5%
. 7905
33.3%
1 7161
30.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23715
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 8649
36.5%
. 7905
33.3%
1 7161
30.2%

Edema_S
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size381.6 KiB
0.0
7506 
1.0
 
399

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters23715
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7506
95.0%
1.0 399
 
5.0%

Length

2024-02-12T00:31:38.524463image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-12T00:31:38.596340image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7506
95.0%
1.0 399
 
5.0%

Most occurring characters

ValueCountFrequency (%)
0 15411
65.0%
. 7905
33.3%
1 399
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15810
66.7%
Other Punctuation 7905
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15411
97.5%
1 399
 
2.5%
Other Punctuation
ValueCountFrequency (%)
. 7905
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 23715
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15411
65.0%
. 7905
33.3%
1 399
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23715
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15411
65.0%
. 7905
33.3%
1 399
 
1.7%

Edema_Y
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size381.6 KiB
0.0
7560 
1.0
 
345

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters23715
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row1.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7560
95.6%
1.0 345
 
4.4%

Length

2024-02-12T00:31:38.669679image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-12T00:31:38.740886image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7560
95.6%
1.0 345
 
4.4%

Most occurring characters

ValueCountFrequency (%)
0 15465
65.2%
. 7905
33.3%
1 345
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15810
66.7%
Other Punctuation 7905
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15465
97.8%
1 345
 
2.2%
Other Punctuation
ValueCountFrequency (%)
. 7905
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 23715
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15465
65.2%
. 7905
33.3%
1 345
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23715
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15465
65.2%
. 7905
33.3%
1 345
 
1.5%

Drug
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size381.6 KiB
1.0
4010 
0.0
3895 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters23715
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 4010
50.7%
0.0 3895
49.3%

Length

2024-02-12T00:31:38.816024image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-12T00:31:38.887590image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0 4010
50.7%
0.0 3895
49.3%

Most occurring characters

ValueCountFrequency (%)
0 11800
49.8%
. 7905
33.3%
1 4010
 
16.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15810
66.7%
Other Punctuation 7905
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 11800
74.6%
1 4010
 
25.4%
Other Punctuation
ValueCountFrequency (%)
. 7905
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 23715
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 11800
49.8%
. 7905
33.3%
1 4010
 
16.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23715
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 11800
49.8%
. 7905
33.3%
1 4010
 
16.9%

Sex
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size381.6 KiB
0.0
7336 
1.0
 
569

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters23715
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7336
92.8%
1.0 569
 
7.2%

Length

2024-02-12T00:31:38.963532image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-12T00:31:39.358042image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7336
92.8%
1.0 569
 
7.2%

Most occurring characters

ValueCountFrequency (%)
0 15241
64.3%
. 7905
33.3%
1 569
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15810
66.7%
Other Punctuation 7905
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15241
96.4%
1 569
 
3.6%
Other Punctuation
ValueCountFrequency (%)
. 7905
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 23715
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15241
64.3%
. 7905
33.3%
1 569
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23715
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15241
64.3%
. 7905
33.3%
1 569
 
2.4%

Ascites
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size381.6 KiB
0.0
7525 
1.0
 
380

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters23715
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7525
95.2%
1.0 380
 
4.8%

Length

2024-02-12T00:31:39.432187image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-12T00:31:39.502476image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7525
95.2%
1.0 380
 
4.8%

Most occurring characters

ValueCountFrequency (%)
0 15430
65.1%
. 7905
33.3%
1 380
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15810
66.7%
Other Punctuation 7905
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15430
97.6%
1 380
 
2.4%
Other Punctuation
ValueCountFrequency (%)
. 7905
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 23715
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15430
65.1%
. 7905
33.3%
1 380
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23715
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15430
65.1%
. 7905
33.3%
1 380
 
1.6%

Hepatomegaly
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size381.6 KiB
1.0
4042 
0.0
3863 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters23715
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row1.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 4042
51.1%
0.0 3863
48.9%

Length

2024-02-12T00:31:39.580207image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-12T00:31:39.652475image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0 4042
51.1%
0.0 3863
48.9%

Most occurring characters

ValueCountFrequency (%)
0 11768
49.6%
. 7905
33.3%
1 4042
 
17.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15810
66.7%
Other Punctuation 7905
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 11768
74.4%
1 4042
 
25.6%
Other Punctuation
ValueCountFrequency (%)
. 7905
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 23715
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 11768
49.6%
. 7905
33.3%
1 4042
 
17.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23715
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 11768
49.6%
. 7905
33.3%
1 4042
 
17.0%

Spiders
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size381.6 KiB
0.0
5966 
1.0
1939 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters23715
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row1.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 5966
75.5%
1.0 1939
 
24.5%

Length

2024-02-12T00:31:39.729028image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-12T00:31:39.801897image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 5966
75.5%
1.0 1939
 
24.5%

Most occurring characters

ValueCountFrequency (%)
0 13871
58.5%
. 7905
33.3%
1 1939
 
8.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15810
66.7%
Other Punctuation 7905
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 13871
87.7%
1 1939
 
12.3%
Other Punctuation
ValueCountFrequency (%)
. 7905
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 23715
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 13871
58.5%
. 7905
33.3%
1 1939
 
8.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23715
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 13871
58.5%
. 7905
33.3%
1 1939
 
8.2%

Stage
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size381.6 KiB
2.0
3153 
3.0
2703 
1.0
1652 
0.0
397 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters23715
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row3.0
4th row2.0
5th row3.0

Common Values

ValueCountFrequency (%)
2.0 3153
39.9%
3.0 2703
34.2%
1.0 1652
20.9%
0.0 397
 
5.0%

Length

2024-02-12T00:31:39.878052image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-12T00:31:39.953406image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
2.0 3153
39.9%
3.0 2703
34.2%
1.0 1652
20.9%
0.0 397
 
5.0%

Most occurring characters

ValueCountFrequency (%)
0 8302
35.0%
. 7905
33.3%
2 3153
 
13.3%
3 2703
 
11.4%
1 1652
 
7.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15810
66.7%
Other Punctuation 7905
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 8302
52.5%
2 3153
 
19.9%
3 2703
 
17.1%
1 1652
 
10.4%
Other Punctuation
ValueCountFrequency (%)
. 7905
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 23715
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 8302
35.0%
. 7905
33.3%
2 3153
 
13.3%
3 2703
 
11.4%
1 1652
 
7.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23715
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 8302
35.0%
. 7905
33.3%
2 3153
 
13.3%
3 2703
 
11.4%
1 1652
 
7.0%

Interactions

2024-02-12T00:31:33.973451image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:21.750339image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:22.819079image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:23.837582image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:24.764141image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:25.914966image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:26.864546image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:27.844278image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:28.839272image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:29.958113image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:30.914885image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:31.835379image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:32.765147image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:34.048597image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:21.824170image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:22.892991image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:23.907002image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:24.835996image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:25.995035image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:26.935624image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:27.919247image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:28.906842image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:30.031062image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:30.983642image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:31.907780image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:32.838183image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:34.127882image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:21.902019image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:22.972631image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:23.983445image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:24.913725image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:26.072469image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:27.014509image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:28.006644image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:28.986330image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:30.110969image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:31.062336image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:31.985170image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:32.913407image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:34.199892image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:21.975162image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:23.048079image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:24.054838image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:24.988948image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:26.143544image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:27.087592image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:28.083478image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:29.062602image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:30.182175image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:31.131120image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:32.060239image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:32.983903image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:34.276447image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:22.050455image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:23.126067image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:24.128391image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:25.064641image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:26.216811image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:27.163667image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:28.161546image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:29.336295image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:30.257431image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:31.204955image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:32.132546image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:33.060812image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:34.350186image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:22.122789image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:23.208188image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:24.199209image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:25.137980image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:26.290066image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:27.235821image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:28.236631image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:29.404624image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:30.331206image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:31.275554image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:32.203557image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:33.131676image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:34.424059image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:22.195992image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:23.297635image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:24.272248image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:25.215024image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:26.364383image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:27.317625image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:28.316205image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:29.476331image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:30.405442image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:31.348312image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:32.275733image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:33.206980image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:34.501181image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:22.274421image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:23.387152image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:24.348686image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:25.299572image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:26.444319image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:27.395627image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:28.394654image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:29.553935image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:30.482617image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:31.424192image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:32.354310image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:33.283905image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:34.576055image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:22.340205image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:23.461552image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:24.412916image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:25.369093image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:26.508723image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:27.461073image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:28.464678image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:29.617450image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:30.557309image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:31.487466image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:32.418195image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:33.352272image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:34.649941image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:22.412290image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:23.543010image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:24.483901image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:25.442016image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:26.584622image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:27.536801image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:28.542401image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:29.686291image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:30.631464image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:31.560553image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:32.488604image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:33.424045image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:34.719867image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:22.481610image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:23.616062image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:24.555507image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:25.688902image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:26.652286image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:27.609522image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:28.616005image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:29.753010image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:30.700260image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:31.627727image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:32.558794image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:33.493691image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:34.796355image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:22.675461image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:23.687312image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:24.622855image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:25.762097image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:26.722416image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:27.682746image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:28.688493image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:29.822175image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:30.770911image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:31.696613image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:32.625560image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:33.570916image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:34.866705image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:22.747774image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:23.765254image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:24.691682image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:25.836004image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:26.792081image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:27.757123image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:28.761806image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:29.888314image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:30.842190image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:31.763651image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:32.694676image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T00:31:33.638991image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Correlations

2024-02-12T00:31:40.032255image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
AgeAge_YearsAlbuminAlk_PhosAscitesBilirubinCholesterolCopperDiagnosis_DateDrugEdema_NEdema_SEdema_YHepatomegalyN_DaysPlateletsProthrombinSGOTSexSpidersStageStatusTryglicerides
Age1.0000.999-0.079-0.0410.1910.055-0.0770.0340.9580.1290.1680.0940.1720.126-0.104-0.0970.134-0.0370.1460.0820.1040.1730.021
Age_Years0.9991.000-0.079-0.0410.2130.055-0.0760.0350.9580.1340.1940.1060.1890.141-0.103-0.0960.133-0.0370.1480.0840.1040.1830.021
Albumin-0.079-0.0791.000-0.1670.443-0.304-0.054-0.236-0.1360.1050.3470.0890.4350.2680.2410.125-0.167-0.2200.0600.2300.1530.223-0.113
Alk_Phos-0.041-0.041-0.1671.0000.1220.3330.3200.281-0.0010.0530.1290.0990.1260.210-0.1470.0520.0920.4270.0360.0990.0630.1710.195
Ascites0.1910.2130.4430.1221.0000.263-0.0960.2220.1980.0450.5260.0870.6570.184-0.260-0.1830.2640.1260.0330.2090.1950.2760.110
Bilirubin0.0550.055-0.3040.3330.2631.0000.3250.5860.1550.0840.3410.1600.3900.335-0.405-0.1680.2680.4990.1060.3210.1370.3480.316
Cholesterol-0.077-0.076-0.0540.320-0.0960.3251.0000.255-0.0390.0790.0440.0000.0520.136-0.1230.124-0.0500.3470.0490.0720.0350.1560.332
Copper0.0340.035-0.2360.2810.2220.5860.2551.0000.1180.0720.2980.1350.3060.312-0.338-0.1260.2090.4380.1790.2820.1380.3240.341
Diagnosis_Date0.9580.958-0.136-0.0010.1980.155-0.0390.1181.0000.0990.2510.1110.2420.181-0.360-0.1250.1570.0400.1690.1250.1180.2050.078
Drug0.1290.1340.1050.0530.0450.0840.0790.0720.0991.0000.0250.0000.0330.0620.0040.0200.0280.0410.0430.0000.0270.0220.073
Edema_N0.1680.1940.3470.1290.5260.3410.0440.2980.2510.0251.0000.7140.6620.2240.2580.177-0.297-0.1140.0510.2570.2310.328-0.071
Edema_S0.0940.1060.0890.0990.0870.1600.0000.1350.1110.0000.7141.0000.0470.135-0.109-0.0700.1500.0540.0700.1330.1160.1710.021
Edema_Y0.1720.1890.4350.1260.6570.3900.0520.3060.2420.0330.6620.0471.0000.174-0.252-0.1780.2640.1050.0000.2230.2060.2860.079
Hepatomegaly0.1260.1410.2680.2100.1840.3350.1360.3120.1810.0620.2240.1350.1741.000-0.293-0.2000.2510.2310.0650.3290.5260.3960.181
N_Days-0.104-0.1030.241-0.147-0.260-0.405-0.123-0.338-0.3600.0040.258-0.109-0.252-0.2931.0000.155-0.150-0.2810.0860.2710.1750.349-0.209
Platelets-0.097-0.0960.1250.052-0.183-0.1680.124-0.126-0.1250.0200.177-0.070-0.178-0.2000.1551.000-0.179-0.0360.0570.2120.1340.172-0.013
Prothrombin0.1340.133-0.1670.0920.2640.268-0.0500.2090.1570.028-0.2970.1500.2640.251-0.150-0.1791.0000.1340.0890.3110.1980.3010.008
SGOT-0.037-0.037-0.2200.4270.1260.4990.3470.4380.0400.041-0.1140.0540.1050.231-0.281-0.0360.1341.0000.0760.1910.0930.2420.186
Sex0.1460.1480.0600.0360.0330.1060.0490.1790.1690.0430.0510.0700.0000.0650.0860.0570.0890.0761.0000.0240.0380.1300.084
Spiders0.0820.0840.2300.0990.2090.3210.0720.2820.1250.0000.2570.1330.2230.3290.2710.2120.3110.1910.0241.0000.3080.3240.078
Stage0.1040.1040.1530.0630.1950.1370.0350.1380.1180.0270.2310.1160.2060.5260.1750.1340.1980.0930.0380.3081.0000.2730.078
Status0.1730.1830.2230.1710.2760.3480.1560.3240.2050.0220.3280.1710.2860.3960.3490.1720.3010.2420.1300.3240.2731.0000.194
Tryglicerides0.0210.021-0.1130.1950.1100.3160.3320.3410.0780.073-0.0710.0210.0790.181-0.209-0.0130.0080.1860.0840.0780.0780.1941.000

Missing values

2024-02-12T00:31:34.986354image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-02-12T00:31:35.223815image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

N_DaysAgeBilirubinCholesterolAlbuminCopperAlk_PhosSGOTTrygliceridesPlateletsProthrombinStatusDiagnosis_DateAge_YearsEdema_NEdema_SEdema_YDrugSexAscitesHepatomegalySpidersStage
0999215322.3316.03.35172.01601.0179.8063.0394.09.7220533591.00.00.00.01.00.00.00.02.0
12574192370.9364.03.5463.01440.0134.8588.0361.011.0016663531.00.00.01.00.00.00.00.02.0
23428137273.3299.03.55131.01029.0119.3550.0199.011.7210299380.00.01.01.00.00.01.01.03.0
32576184600.6256.03.5058.01653.071.3096.0269.010.7015884511.00.00.01.00.00.00.00.02.0
4788166581.1346.03.6563.01181.0125.5596.0298.010.6015870461.00.00.01.00.00.01.00.03.0
5703192700.6227.03.4634.06456.260.6368.0213.011.5218567531.00.00.00.00.00.01.00.02.0
61300177031.0328.03.3543.01677.0137.9590.0291.09.8016403481.00.00.01.00.00.00.00.02.0
71615212810.6273.03.9436.0598.052.70214.0227.09.9019666581.00.00.01.00.00.01.00.02.0
82050206840.7360.03.6572.03196.094.55154.0269.09.8018634571.00.00.00.00.00.00.00.01.0
92615150090.9478.03.6039.01758.0171.00140.0234.010.6012394411.00.00.00.00.00.00.00.01.0
N_DaysAgeBilirubinCholesterolAlbuminCopperAlk_PhosSGOTTrygliceridesPlateletsProthrombinStatusDiagnosis_DateAge_YearsEdema_NEdema_SEdema_YDrugSexAscitesHepatomegalySpidersStage
78951433141610.5291.04.2437.01065.085.25195.0201.010.6012728391.00.00.01.00.00.00.00.01.0
78961271138060.6328.03.9531.0663.052.70166.0344.010.4012535381.00.00.01.00.00.00.00.02.0
78971455168983.4279.03.53143.0671.0113.1572.0151.09.8015443461.00.00.01.00.00.00.01.02.0
789877198845.1178.02.75464.01020.0120.90118.080.012.3219807540.00.01.01.00.01.01.00.03.0
78991413246221.3262.03.7365.02045.089.9078.0181.011.0223209671.00.00.01.00.00.00.00.02.0
79001166168390.8309.03.5638.01629.079.05224.0344.09.9015673461.00.00.00.00.00.00.00.01.0
79011492170310.9260.03.4362.01440.0142.0078.0277.010.0015539471.00.00.01.00.00.01.00.03.0
79021576258732.0225.03.1951.0933.069.7562.0200.012.7224297710.01.00.00.00.00.00.01.01.0
79033584229600.7248.02.7532.01003.057.35118.0221.010.6219376631.00.00.00.01.00.01.00.03.0
79041978192370.7256.03.2322.0645.074.4085.0336.010.3017259531.00.00.00.00.00.00.00.02.0